Metabolic Networks

With the advent of next-generation sequencing techniques, it is now easier to obtain high throughput data revealing what is encoded in an organism's genome. From this information the systems biology community can build metabolic networks at the genome scale and use various techniques to study the distribution of reaction rates at the steady state, identify alternative pathways, characterize gene knock-outs and much more.

Because of their high dimension and therefore complexity, these models lack detailed information on, e.g., the kinetics of the reactions. Our research is aimed at integrating the static constraint based modelling with other dynamic modelling techniques. That would allow us to capture the dynamics of processes like gene expression switch depending on the availability of particular metabolites.

Our models are currently developed for bacteria and microalgae and are intended to study, understand and predict the metabolic processes that take place when these organisms interact. If we are able to grasp what are the mechanisms that determine the success of a particular community of organisms we can then design a synthetic ecological niche for convenient and robust growth of the species.

We work both on computational models and laboratory experiments to test the interplay between simulation predictions and real data, as well as to verify key assumptions of the models and we established various collaborations.

Contact: Antonella Succurro, Oliver Ebenhöh

Key publications

  • Succurro, Segrè and Ebenhöh, in preparation

Further reading

Verantwortlich für den Inhalt: E-Mail sendenPascal Spinnrath